{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yeo-Johnson transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for Q-Q plots\n",
    "import scipy.stats as stats\n",
    "\n",
    "# the dataset for the demo\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "# with open-source packages\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from feature_engine.transformation import YeoJohnsonTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# drop lat and lon\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bmZm1IReuzXpcRNwObKyw6ALgs0Dk0mYCV0RmObC7pH2Ao4FlEbExIjYBy6hQYDczM+t2nkRmGNUmgDHrdpJmAusi4meS8ovGA0/kXq9NadXSzczMeooL12a2FUk7Ap8jaxLSiP3PJmtSQl9fH6VSaavlAwMDQ9KazTEUF8OcKZuHpNWyz3b4HNopDjNrfy5cm9lgvwdMAsq11hOAuyUdAqwD9s2tOyGlrQOmD0ovVdp5RCwAFgBMmzYtpk+fvtXyUqnE4LRmcwzFxXBqhTuAa04e/T7b4XNopzjMrP25zbWZbSUiVkfEb0fExIiYSNbE46CIeBJYApySRg05DHguItYDNwNHSdojdWQ8KqWZWUEqjewjaU9Jy9IoPcvKHYnHMrKPpIMlrU7bXKRBbcLMbHRcuDbrcZKuAn4C7CdpraTTh1l9KfAo0A98G/gkQERsBM4F7kyPL6U0MyvO5QztKDwXuDUiJgO3ptcwtpF9LgE+ltvOnZLNxsDNQsx6XEScNMLyibnnAZxRZb2FwMJCgzOz10XE7ZImDkqeyZYmWYvImmOdRW5kH2C5pPLIPtNJI/sASFoGzJBUAnZNowAh6QrgeOCmxh2RWXdyzbWZmVnn6ktNswCeBPrS81pH9hmfng9ON7MauebazMysC0RESIqR16zPSCP+5A0MDDBnyqsVl3Xj6Cu9NqpMrx3vaLlw3SKVxtBeM++4FkRiZmYd7ClJ+0TE+tTsY0NKr3Vkn3Xp+eD1hxhpxJ+8UqnE/DterLisllFjOkWvjSrTa8c7Wi5cm5mZjUKbVoosAWYB89Lf63PpZ0paTNZ58blUAL8Z+PtcJ8ajgLMjYqOk59MoQCuAU4CLm3kgZt3ChWszM7MOkEb2mQ7sLWkt2agf84Br0ig/jwMnpNWXAseSjezzEvBRyEb2kVQe2Qe2Htnnk2QjkuxA1pHRnRnNxsCFazMzsw4wzMg+R1RYt+aRfSLiLuDAemI0szpGC5G0r6TbJD0g6X5Jn07phQ1ob2ZmZmbWSeqpud4MzImIuyXtAqxM42WeSjag/TxJc8kGtD+LrQe0P5RssPpDcwPaTwMi7WdJRGyqIzYzs5ao1C4X2qJtrpmZNcGYa64jYn1E3J2evwA8SDYm5kyygexJf49Pz18f0D4NUl8e0P5o0oD2qUC9DM8KZWZmZmYdqJBJZNKMUe8k62Fc1ID2ZmZmZmYdpe4OjZJ2Br4HfCYinpf0+rKiB7SvNHB9IwcwnzNlc0P2W81Ix9Erg7X7OM3MzKxT1VW4lrQtWcH6yoi4LiUXNaD9EJUGrm/kAOanVmk72SgjDajfK4O1+zjNzMysU9UzWoiAy4AHI+KruUXlAe1h6ID2p6RRQw4jDWgP3AwcJWmPNLLIUSnNzBpM0kJJGyTdl0v7R0k/T6P6fF/S7rllZ6cRfx6SdHQufUZK608dmc3MzHpSPW2u3wV8BDhc0qr0OJZsQPsjJT0MvC+9hmxA+0fJBrT/Ntlg9aTB68sD2t/J1gPam1ljXc7QDsTLgAMj4u3AfwFnA0jaHzgROCBt801J4ySNA75BNiLQ/sBJaV0zM7OeM+ZmIRFxB6AqiwsZ0N7MGisibk8dkvNpt+ReLgc+lJ7PBBZHxMvAY5L6gUPSsv6IeBQgTbc8E3igkbGbmZm1I8/QaGbDOQ24Oj0fT1bYLsuP7DN4xJ9Dq+2wUsfkvHbo6FlPDNU6Qte6v07/HMoqfR617LMdPodyHHOmvDokvR1iM7P24sK1mVUk6fNkk0VdWeR+K3VMzmuHjp71xFCtI/RIHZaLjKEoRcRQ6fOo5bNoh8+hHMf8O14ckl7r/9XMup8L12Y2hKRTgfcDR6QmXVB9xB+GSbcWaeRMkZX27RkozcwyhUwiY2bdQ9IM4LPAByLipdyiJcCJkraXNAmYDPyUrCPyZEmTJG1H1ulxSbPjNjMzaweuuTbrYZKuIhtnfm9Ja4FzyEYH2R5YliaFWh4Rn4iI+yVdQ9ZRcTNwRkS8mvZzJtkQmuOAhRFxf9MPxszMrA24cG3WwyLipArJlw2z/nnAeRXSl5INt2lmZtbT3CzEzMzMzKwgLlybmZmZmRXEhWszMzMzs4K4cG1mZtbhJP2lpPsl3SfpKklvSCP4rJDUL+nqNJoPacSfq1P6ivwsrZLOTukPSTq6ZQdk1sFcuDYzM+tgksYDfwFMi4gDyUbtORE4H7ggIt4KbAJOT5ucDmxK6Rek9ZC0f9ruAGAG8E1J45p5LGbdwIVrMzOzzrcNsIOkbYAdgfXA4cC1afki4Pj0fGZ6TVp+hLJxN2cCiyPi5Yh4DOgHDmlO+Gbdw4VrMzOzDhYR64CvAL8gK1Q/B6wEno2IzWm1tcD49Hw88ETadnNaf698eoVtzGyUPM61mZlZB5O0B1mt8yTgWeBfyZp1NOr9ZgOzAfr6+iiVSlXXHRgYYM6UVysuG267TjUwMNCVx1VNrx3vaLlwbWbWIhPn3lgxfc2845ociXW49wGPRcQvASRdB7wL2F3SNql2egKwLq2/DtgXWJuakewGPJNLL8tv87qIWAAsAJg2bVpMnz69amClUon5d7xYcdmak6tv16lKpRLDfR7dpteOd7RcuDYzM+tsvwAOk7Qj8CvgCOAu4DbgQ8BiYBZwfVp/SXr9k7T8RxERkpYA35X0VeBNwGTgp40KutKPS/+wtG7gwrWZmVkHi4gVkq4F7gY2A/eQ1SzfCCyW9OWUdlna5DLgO5L6gY1kI4QQEfdLugZ4IO3njIio3KbDzKpyh0azHidpoaQNku7Lpe0paZmkh9PfPVK6JF2UxsG9V9JBuW1mpfUfljSrFcdi1qsi4pyI+P2IODAiPpJG/Hg0Ig6JiLdGxIcj4uW07q/T67em5Y/m9nNeRPxeROwXETe17ojMOpcL12Z2OUM7P80Fbo2IycCt6TXAMWS3iieTdWi6BLLCOHAOcCjZ0F3nlAvkZmZmvcSFa7MeFxG3k90azsuPgzt4fNwrIrOcrMPUPsDRwLKI2BgRm4BlNHC0AjMzs3blwrWZVdIXEevT8yeBvvS82ji4Hh/XzMwMd2hsKx6Wy9pRGkUgitrfSGPktsO4qfXEMGfK5orplfY33Lr1fg61xFFNtRgq7bvafmtZt5YYmq3aeM3tEJuZtRcXrs2skqck7RMR61Ozjw0pvdo4uOuA6YPSS5V2PNIYue0wbmo9MZxa7UdyhTF9h1u33s+hljiqqRZDpX1X228t69YSQ7NVG6+5G8dqNrP61NUspMooA1+UtE7SqvQ4Nrfs7DTKwEOSjs6lz0hp/ZLmDn4fM2u68ji4MHR83FPSqCGHAc+l5iM3A0dJ2iN1ZDwqpZmZmfWUemuuLwe+DlwxKP2CiPhKPkHS/mRjaR5ANjj9DyW9LS3+BnAkWTvNOyUtiYgH6ozNzEZB0lVktc57S1pLNurHPOAaSacDjwMnpNWXAscC/cBLwEcBImKjpHOBO9N6X4qIwZ0kzczMul5dheuIuF3SxFGuPhNYnMbZfCwNXn9IWtZfHmdT0uK0rgvXZk0QESdVWXREhXUDOKPKfhYCCwsMzRrAs+KZmTVWo9pcnynpFLLpV+ekobnGA8tz6+RHExg8ysChDYqrqmqdCc3MzMzMRqsRhetLgHOBSH/nA6cVseNKowwU1ZO8Ws/6dlA+vnbpNd9oPk4zMzPrVIUXriPiqfJzSd8Gbkgvq40ywDDpg/c9ZJSBonqSV+tZ3w7KvdHbpdd8o/k4zdrD6nXPNeTc6GFHzaybFT6JTBq2q+yDQHkkkSXAiZK2lzSJbPrkn5J1gJosaZKk7cg6PS4pOi4zMzMzs0arq+a6yigD0yVNJWsWsgb4OEBE3C/pGrKOipuBMyLi1bSfM8mG7RoHLIyI++uJy8ysk02ceyNzpmweUmvsml0zs/ZX72ghlUYZuGyY9c8DzquQvpRsiC8zMzMzs45VeLMQMzMzM7Ne5cK1mZmZmVlBXLg2MzMzMyuIC9dmZmZmZgVx4drMzMzMrCCNmv7czMwKVmnyFQ/PZwCSdgcuBQ4kGwr3NOAh4GpgItnQuCdExCZJAi4EjgVeAk6NiLvTfmYBX0i7/XJELGreUZh1B9dcd4CJc29k4twbWb3uudefV5vhzKwokv5S0v2S7pN0laQ3pMmeVkjql3R1mviJNDnU1Sl9haSJLQ7frNdcCPwgIn4feAfwIDAXuDUiJgO3ptcAx5BN5DYZmA1cAiBpT7L5Kg4FDgHOkbRHMw/CrBu4cG1mQ0gaD/wFMC0iDiSb4OlE4Hzggoh4K7AJOD1tcjqwKaVfkNYzsyaQtBvwHtI8ExHxSkQ8C8wEyjXPi4Dj0/OZwBWRWQ7snmZXPhpYFhEbI2ITsAyY0bQDMesSLlybWTXbADtI2gbYEVgPHA5cm5YPvliXL+LXAkekW89m1niTgF8C/yzpHkmXStoJ6IuI9WmdJ4G+9Hw88ERu+7UprVq6mdXAba7NbIiIWCfpK8AvgF8BtwArgWcjYnNaLX/hff2iHBGbJT0H7AU8PXjfkmaT3Yqmr6+PUqm01fKBgYEhac1WTwxzpmyumF5pf9XWBejbYfjlw+13pH2Pdh+jjaGRcbTD96Ecx5wprw5Jb4fYyK7lBwGfiogVki5kSxMQACIiJEURbzZSHs6r9rlV0yaf55i1y/e1WXrteEfLhWszGyK1s5xJViP2LPCvFHR7OCIWAAsApk2bFtOnT99qealUYnBas9UTw6lV+kOsOXno/qqtC1mhdP7qkU/RlfY70r5Hu4+Lr7x+VDE0Mo52+D6U45h/x4tD0qsdd5OtBdZGxIr0+lqywvVTkvaJiPWp2ceGtHwdsG9u+wkpbR0wfVB6afCbjZSH86p9btW0yec5Zu3yfW2WXjve0XKzEDOr5H3AYxHxy4j4DXAd8C6ytpnl0lb5ggy5i3VavhvwTHNDNutNEfEk8ISk/VLSEcADwBJgVkqbBVyfni8BTlHmMOC51HzkZuAoSXukH9hHpTQzq4Frrs2skl8Ah0nakaxZyBHAXcBtwIeAxQy9WM8CfpKW/ygiCrkFbWaj8ingyjSCz6PAR8kq0K6RdDrwOHBCWncp2TB8/WRD8X0UICI2SjoXuDOt96WI2Ni8QzDrDi5cm9kQqd3mtcDdwGbgHrLbwDcCiyV9OaVdlja5DPiOpH5gI9nIImbWJBGxCphWYdERFdYN4Iwq+1kILCw0OLMe48K1mVUUEeeQjXmb9yjZ+LeD1/018OFmxGVmZtbO3ObazMzMzKwgLlybmZmZmRXEzULMzKzjTKw25OG845ociZnZ1ly4NjMbo2oFPDMz611uFmJmZmZmVhDXXJuZNYFruc3MeoML12ZmVjf/eDAzy7hwbWZmbaFSAX3OlM1Mb34oZmZjVleba0kLJW2QdF8ubU9JyyQ9nP7ukdIl6SJJ/ZLulXRQbptZaf2HJc2qJyYzMzMzs1apt0Pj5cCMQWlzgVsjYjJwa3oNcAwwOT1mA5dAVhgnmwXuULKZ384pF8jNzMzMzDpJXc1CIuJ2SRMHJc+E1+/iLQJKwFkp/YqICGC5pN0l7ZPWXRYRGwEkLSMrsF9VT2xmZtZ7PP61mbVaI4bi64uI9en5k0Bfej4eeCK33tqUVi3dzFoo/QC+VtLPJT0o6Q/H0uzLzMyslzS0Q2NEhKQoan+SZpM1KaGvr49SqcTAwAClUqnufc+ZsrnufTRa3w5bx1nEcbejov6n7a4DjvNC4AcR8SFJ2wE7Ap8ja/Y1T9JcsmZfZ7F1s69DyZp9HdqasM3MzFqnEYXrpyTtExHrU7OPDSl9HbBvbr0JKW0dbNUZfAJZU5IhImIBsABg2rRpMX36dEqlEtOnT6+0ek1O7YBhpOZM2cz81Vv+ZWtOnt66YBqoqP9pu2vn45S0G/Ae4FSAiHgFeEVSTc2+cnexzMzMekIjmoUsAcojfswCrs+ln5JuHx8GPJcuvDcDR0naI91iPiqlmVnrTAJ+CfyzpHskXSppJ2pv9mVmZtZT6qq5lnQVWS3W3pLWko36MQ+4RtLpwOPACWn1pcCxQD/wEvBRgIjYKOlc4M603pfKnRvNrGW2AQ4CPhURKyRdyJaRf4CxN/uq1Lwrrx2ay4w2hkY2JxvcDKyaanHWElu1fYw2hkbq26FyfLXGVe93amBggDlTXi18v2bWfeodLeSkKouOqLBuAGdU2c9CYGE9sfSiSr3i3SPeCrIWWBsRK9Lra8kK17U2+xqiUvOuvHZoLjPaGBrZnGxwM7BqqjUPqyW2avu4+MrrRxVDI82ZspkTKvwvav3s621GVyqVmH/Hi4Xv18y6TyOahZhZh4uIJ4EnJO2Xko4AHqD2Zl9m1iSSxqVmXDek15MkrUij+FydOiYjafv0uj8tn5jbx9kp/SFJR7foUMw6mgvXZlbNp4ArJd0LTAX+nqzZ15GSHgbel15D1uzrUbJmX98GPtn0aM3s08CDudfnAxdExFuBTcDpKf10YFNKvyCth6T9gROBA8jmm/impHFNit2sa7T2fp+Zta2IWAVMq7CopmZfZtZ4kiYAxwHnAX8lScDhwJ+lVRYBXyQbJnNmeg5Zk6+vp/VnAosj4mXgMUn9ZDMn/6RJh2HWFVxzbWZm1vm+BnwWeC293gt4NiLKPT/zI/i8PrpPWv5cWt+j/pgVwDXXZmZmHUzS+4ENEbFS0vQmvN+wI/7kVRtlpZpOH32lHUY7aqZeO97RcuHazMyss70L+ICkY4E3ALuSzbC6u6RtUu10fgSf8ug+ayVtA+wGPMMoR/0ZacSfvGqjrFTT6aOvtMNoR83Ua8c7Wm4WYmaWTJx7IxPn3sjqdc+9/rzSkJdm7SQizo6ICRExkaxD4o8i4mTgNuBDabXBo/uUR/35UFo/UvqJaTSRScBk4KdNOgyzruGaazMzs+50FrBY0peBe4DLUvplwHdSh8WNZAVyIuJ+SdeQDbu5GTgjIkbfpsPMABeuzczMukZElIBSev4o2Wgfg9f5NfDhKtufRzbiiJmNkZuFmJmZmZkVxDXXZmYdzG3Czczai2uuzczMzMwK4pprM7MeV632e86UJgdiZtYFXLjuMtUukmvmHdfkSMzMzMx6j5uFmFlVksZJukfSDen1JEkrJPVLulrSdil9+/S6Py2f2NLAzczMWsSFazMbzqeBB3OvzwcuiIi3ApuA01P66cCmlH5BWs/MzKznuHBtZhVJmgAcB1yaXgs4HLg2rbIIOD49n5lek5YfkdY3MzPrKW5zbWbVfA34LLBLer0X8GxEbE6v1wLj0/PxwBMAEbFZ0nNp/aebFm2NPISdmZk1ggvXZjaEpPcDGyJipaTpBe97NjAboK+vj1KptNXygYGBIWmjsXrdcxXTp4zfrWL6nCmbK6YD9O2w9fJq8Qy3j3oNjqEV2iWGSp9/rXGN5TuVNzAwwJwpQ2cCr3e/ZtZ9XLg2s0reBXxA0rHAG4BdgQuB3SVtk2qvJwDr0vrrgH2BtZK2AXYDnqm044hYACwAmDZtWkyfPn2r5aVSicFpo3FqtZFyTq68r2rrQ1Zwm796y+lxLPuo1+AYWqFdYjihwveh1s++2v9wtEqlEvPveLHw/ZpZ93GbazMbIiLOjogJETEROBH4UUScDNwGfCitNgu4Pj1fkl6Tlv8oIqKJIZuZmbUF11ybWS3OAhZL+jJwD3BZSr8M+I6kfmAjWYHcrK15XgAza4SeK1y7E5NZbSKiBJTS80eBQyqs82vgw00NzMzMrA01rFmIpDWSVktaJemulLanpGWSHk5/90jpknRRmoDiXkkHNSouMzMzM7NGaXSb6/dGxNSImJZezwVujYjJwK3pNcAxwOT0mA1c0uC4zMzMzMwK1+wOjfmJJgZPQHFFZJaTjUiwT5NjMzMzMzOrSyML1wHcImllGtcWoC8i1qfnTwJ96fnrE1Ak+ckpzMzMzMw6QiM7NL47ItZJ+m1gmaSf5xdGREiqaaiuSpNP1DrhRKsnRKhHPRM6XHzl9RXTq02w0UpjnUSk0/TKcZqZmfWShhWuI2Jd+rtB0vfJRhh4StI+EbE+NfvYkFYvT0BRlp+cIr/PIZNP1DrhRCMnfWi0Rkzo0I4TIIx1EpFO0yvHaWaNJWlf4Aqyu8EBLIiICyXtCVwNTATWACdExCZJIpsU6ljgJeDUiLg77WsW8IW06y9HxCLMrCYNKVxL2gn4rYh4IT0/CvgSWyaamMfQCSjOlLQYOBR4Ltd8xMzMepiHUB3RZmBORNwtaRdgpaRlwKlkgwjMkzSXbBCBs9h6EIFDyQYRODQVxs8BppEV0ldKWhIRm5p+RGYdrFE1133A97Mfx2wDfDcifiDpTuAaSacDjwMnpPWXkv2C7if7Ff3RBsVlZmbWVVJl1Pr0/AVJD5L1W5oJTE+rLSIbr/4scoMIAMsllQcRmA4si4iNAKmAPgO4qmkHY9YFGlK4ThNNvKNC+jPAERXSAzijEbGYmZn1CkkTgXcCK6h9EAEPLmBWgJ6bodHMzGw4lZqhZJ3J2/uSKWln4HvAZyLi+XT3GBjbIALDvM+QwQWqGRgYYM6UV0e9707v5N1rHdV77XhHq73PFGZmFbgNrtnWJG1LVrC+MiKuS8m1DiKwji3NSMrppcHvVWlwgWpKpRLz73hx1MfRjp3sa9FrHdV77XhHy4VrMzPret38gyyN/nEZ8GBEfDW3qKZBBCTdDPy9pD3SekcBZzfjGMy6iQvXZjZEkUN7dYNuLphZV3gX8BFgtaRVKe1zZIXqUQ8iEBEbJZ0L3JnW+1K5c6OZjZ4L12ZWSSFDe7UkcrMeExF3AKqyuKZBBCJiIbCwuOjMek8jpz83sw4VEevLNc8R8QKQH9qrPKnEIuD49Pz1ob0iYjlQHtrLzMysp7hwbWbDqnNoLzMzs57iZiE9rlJb0jXzjmtBJNaOGjG010jDeI1maKdsWLTRqbav4fbRt0Nt79EIjqF9YhguDg9DZmaDuXBtZhUVNLTXECMN4zWaoZ1OraGDYbWhvYbbx5wpm5m/urWnR8fQPjEMF0enDx1nZsVr/RnLzNpOUUN7NTHkqjzSh5mZNZML12ZWSSFDe5mZmfUaF67NbIgih/YyMzPrJS5c2xDVbqO7o6OZmZnZ8DwUn5mZmZlZQVxzbaPmYfvMzMzMhueaazMzMzOzgrhwbWZmZmZWEBeuzczMzMwK0rVtrj1xhJmZmZk1W9cWrq05PGyfmZmZ2RZuFmJmZmZmVhDXXJtZ23LzLjMz6zQuXFtDuLmImZmZ9aK2KVxLmgFcCIwDLo2IeS0OyZpkcEF8zpTNnDpMjaUL6O3L+dis8zkfm9WnLQrXksYB3wCOBNYCd0paEhEPtDYyK1oRt/k7cabISjFfPmOnFkTSOM7HZp3P+disfu3SofEQoD8iHo2IV4DFwMwWx2RmtXE+Nut8zsdmdWqLmmtgPPBE7vVa4NAWxWIdqNkd39q9prxFnI/NOl9L87H761g3aJfC9ahImg3MTi8HJD0E7A083bqomucveuRYO+E4dX79+3jv+aM6zjfX/07tpUo+zmv5/78dvoOOoX1iGC6OUZ4LuiofjyIP5xXy/yvinNskbfF9baJeOd6a8nC7FK7XAfvmXk9IaVuJiAXAgnyapLsiYlpjw2sPvXKsPs6ONeZ8nNcOn4tjcAztGkcTjJiPR8rDeT30uQE+Xsu0S5vrO4HJkiZJ2g44EVjS4pjMrDbOx2adz/nYrE5tUXMdEZslnQncTDb0z8KIuL/FYZlZDZyPzTqf87FZ/dqicA0QEUuBpWPYdFS3prpErxyrj7ND1ZGP89rhc3EMGcewRbvE0XAF5eOynvncEh+voYhodQxmZmZmZl2hXdpcm5mZmZl1vI4uXEuaIekhSf2S5rY6nkaRtEbSakmrJN3V6niKJGmhpA2S7sul7SlpmaSH0989WhljEaoc5xclrUv/11WSjm1ljK3WqvzcDt9BSftKuk3SA5Lul/TpZsch6Q2SfirpZymGv0vpkyStSP+Xq1Mnt4aSNE7SPZJuaEUMlc653XheaqReuD53+/eklnOjMhel//e9kg5qXeSt17GFa22ZovUYYH/gJEn7tzaqhnpvREztwiFvLgdmDEqbC9waEZOBW9PrTnc5Q48T4IL0f52a2jn2pBbn58tp/XdwMzAnIvYHDgPOSMffzDheBg6PiHcAU4EZkg4Dzif7nr4V2ASc3sAYyj4NPJh73YoYBp9zu/G81BA9dn3u5u/J5Yz+3HgMMDk9ZgOXNCnGttSxhWs8RWtXiIjbgY2DkmcCi9LzRcDxzYypEaocp23RsvzcDt/BiFgfEXen5y+QFSzHNzOOyAykl9umRwCHA9c2IwYASROA44BL02s1O4Yquu681EC9fH3umu9JjefGmcAV6TyyHNhd0j5NCbQNdXLhutIUreNbFEujBXCLpJXKZsbqdn0RsT49fxLoa2UwDXZmuoW2sJNvHxag3fJzy76DkiYC7wRWNDuO1BxjFbABWAY8AjwbEZvTKs34v3wN+CzwWnq9VwtiqHTO7aXzUr3aLT83Si9+T6odX6/8z0elkwvXveTdEXEQ2W2XMyS9p9UBNUtkw9l065A2lwC/R3YLfj0wv6XRWEXN/A5K2hn4HvCZiHi+2XFExKsRMZVsVr5DgN9v5PsNJun9wIaIWNnM961g2HNul5+XbPR6+nvS7cdXj04uXI9qquVuEBHr0t8NwPfJLnrd7Kny7aT0d0OL42mIiHgqFWZeA75N9/9fh9Nu+bnp30FJ25IVrK+MiOtaFQdARDwL3Ab8Idnt3fKcCI3+v7wL+ICkNWRNCQ4HLmxyDNXOuT1xXipIu+XnhujR70m14+uJ//lodXLhuiemaJW0k6Rdys+Bo4D7ht+q4y0BZqXns4DrWxhLwwxqj/ZBuv//Opx2y89N/Q6mdsWXAQ9GxFdbEYekN0raPT3fATiSrO33bcCHmhFDRJwdERMiYiLZd+BHEXFyM2MY5pzbE+elgrRbfi5cD39Pqh3fEuCUNGrIYcBzueYjvSciOvYBHAv8F1m7wM+3Op4GHeNbgJ+lx/3ddpzAVWRNIn5D1kbrdLI2lrcCDwM/BPZsdZwNOs7vAKuBe8lOTPu0Os4Wf0Ytyc/t8B0E3k12e/VeYFV6HNvMOIC3A/ekGO4D/jalvwX4KdAP/CuwfZP+L9OBG5odQ7Vzbjeelxr8/+vq63MvfE9qOTcCIhsh5pF0XZvW6vhb+fAMjWZmZmZmBenkZiFmZmZmZm3FhWszMzMzs4K4cG1mZmZmVhAXrs3MzMzMCuLCtZmZmZlZQVy4NjMzMzMriAvXZmZmZmYFceHazMzMzKwgLlybmZmZmRXEhWszMzMzs4K4cG1mZmZmVhAXrs3MzMzMCuLCtZmZmZlZQVy4NjMzMzMriAvXZmZmZmYFceHazMzMzKwgLlybmZmZmRXEhWszMzMzs4K4cG1mZmZmVhAXrs3MzMzMCuLCtZmZmZlZQVy4NjMzMzMriAvXZmZmZmYFceHaCiMpJL211XGY2dYkXS7py3VsPyDpLUXGZGbWrVy47jCSSpI2Sdq+gH2tkfSrdOHcJOlGSfsWEaeZVTco7z2VCr87tzoueP0c8+f5tIjYOSIebVVMZp2gyOtz2t+pklZLeknSk5IukbR7Efu2xnLhuoNImgj8ERDABwra7R9HxM7APsBTwMUF7XcrksY1Yr9mHayc9w4CpgFfaHE8ZjZGRV+fJc0Bzgf+BtgNOAx4M7BM0nb17t8ay4XrznIKsBy4HJglaXtJz0o6sLyCpDemGrHfTq/fL2lVWu8/Jb290o4j4tfAtcD+uX1tL+krkn6Rate+JWmH3PK/kbRe0n9LOi2/v1QTd4mkpZJeBN6bauv+RtK9kl6UdJmkPkk3SXpB0g8l7ZG2f4Okf5H0TIr9Tkl9hX2SZm0iItYBNwEHSvqApPvTd74k6Q/K66X8c7akB1Lt2D9LekNadqqkO/L7rdZMS9Iekm6Q9Mu0nxskTUjLziMrIHw91ap/ffC+JO0m6Yq0/eOSviDpt/JxpPPGJkmPSTqmMZ+cWVsp7PosaVfg74BPRcQPIuI3EbEGOAGYCPyftN44SZ+T9Ei6hq4s332WdICkZZI2puv351L6Vk3EJE2XtDb3uup5xkbPhevOcgpwZXocDewOXAeclFvnBODHEbFB0juBhcDHgb2AfwKWqMItK0k7An9KdnIomwe8DZgKvBUYD/xtWn8G8NfAkcBk4H0V4v0z4DxgF6B84f/faZu3AX9MVqj4HPBGsu/jX6T1ZpH9Wt83xf4J4FfDfThmnShdDI8FXgCuAj5Dlh+WAv8+qJbqZLK8/3tkeWgstd2/BfwzWS3Y75Llq68DRMTngf8AzkxNQc6ssP3FZHnzLcD/IjsvfTS3/FDgIWBv4B+AyyRpDHGadZIir8//E3hD2v51ETFAdl44MiX9Vdr/scCuwGnAS5J2AX4I/AB4E9n1+9YajqWI80xPc+G6Q0h6N9nF8JqIWAk8QlZ4/S5wYm7VchrAbOCfImJFRLwaEYuAl8luL5X9m6RngefIMuw/pvdT2v4vI2JjRLwA/H3uvU4A/jki7ouIF4EvVgj7+oj4fxHxWqoZB7g4Ip5KtXX/AayIiHvS8u8D70zr/YbshPPWFPvKiHi+tk/NrK2V894dwI+BB4AbI2JZRPwG+AqwA9mFtuzrEfFERGwk++F6EjWKiGci4nsR8VLK1+eRFZJHpKx514nA2RHxQqpNmw98JLfa4xHx7Yh4FVhE1uTMd52sazXg+rw38HREbK7wduvTcoA/B74QEQ9F5mcR8QzwfuDJiJgfEb9OeXVFDYdU93mm123T6gBs1GYBt0TE0+n1d1PawcCOkg4lazM9layQCllmnyXpU7n9bEf2S7bs+Ij4YbpozgR+LGl/4DVgR2BlrtJJQLnt9JuAlbn9PF4h5icqpD2Ve/6rCq/Lnbq+Q1ZrvVhZB45/AT6fCh1m3eD4iPhh+YWkS8jlo4h4TdITZHeMyvJ56nG2zsujku5SXQDMAPZIybtIGpcKxMPZG9iWrfP744NifLL8JCJeSuePtuisadYgRV+fnwb2lrRNhQL2Pmk5ZNfIRyrEUy19tOo+z/Q6F647gLJ2zicA4ySVL1zbk912OhC4huyX5VPADak2CrIMcl5EnDfSe6SL6nWS/gl4N9ntqF8BB6Ra5sHWk2Xgst+ttNuR3neYeH5D1ubs75R1FFlKdqv5srHu06zN/Tcwpfwi3T3aF8jnv8F57r/T8xfJfgyXt/2dYd5nDrAfcGhEPClpKnAP2Y9nGD7fPk12V+nNZDXt5TgqnSPMul4jrs+SdiOrxf6TtH05fWfgGLKmlOV9/B5w36BdPMHWNeZ5W50rgErnimrnGRslNwvpDMcDr5J1NpyaHn9A1qziFLJfyX9K1k7qu7ntvg18QtKhyuwk6bjUHmsraflMspqsByPitbT9BbnOF+MlHZ02uQY4VdL+qSbsnCIPWNJ7JU1JNerPk13QXyvyPczazDXAcZKOkLQtWSH4ZeA/c+ucIWmCpD2BzwNXp/SfAQdImpo6H31xmPfZheyH87NpP4Pz7lNk7amHSD/CrwHOk7SLpDeTtfv8lxqO06ybHE/B1+eIeI6sculiSTMkbZsqma4B1pLd2QW4FDhX0uS0j7dL2gu4AdhH0meUdazcJdWeA6wCjpW0Z/oR/pkKx1TtPGOj5MJ1Z5hF1r75FxHxZPlB1gnpZLLmGS+S3bq5qbxRRNwFfCyttwnoB04dtO9/lzRAVoA9D5gVEfenZWelbZZLep6sg8R+ad83AV8DfpTW+VHBx/w7ZKOXPA88SNYm9TvDbmHWwSLiIbJRAC4mqyH+Y7Lh+l7JrfZd4BbgUbLbvl9O2/4X8CWyPPowWzoQV/I1srbcT5N1YP7BoOUXAh9KIwVcVGH7T5Gdbx5N7/Ndso5ZZr2oIdfniPgHshrqr5BdB1eQ1UgfEREvp9W+SlbgviWtcxmwQ6odP5LsHPIk2TnhvWmb75D9GF+TtqtUcK54nrHRU8SY79ybmVmTSFoD/Hm+nbaZWZF8nimGa67NzMzMzAriwrWZmZmZWUHcLMTMzMzMrCCuuTYzMzMzK4gL12ZmZmZmBenYSWT23nvvmDhxYsVlL774IjvttFNzA2oAH0d7acVxrFy58umIeGNT37SJhsvH0NnfnU6OHTo7/naLvZvz8Uh5GNrn/+E4HMdY46g5D0dERz4OPvjgqOa2226ruqyT+DjaSyuOA7gr2iC/NeoxXD6O6OzvTifHHtHZ8bdb7N2cj0fKwxHt8/9wHFtzHFsbLo5a87CbhZiZmZmZFcSFazMzMzOzgrhwbWZmZmZWkBEL15L2lXSbpAck3S/p0yl9T0nLJD2c/u6R0iXpIkn9ku6VdFBuX7PS+g9LmpVLP1jS6rTNRZLUiIM1MzMzM2uk0dRcbwbmRMT+wGHAGZL2B+YCt0bEZODW9BrgGGByeswGLoGsMA6cAxwKHAKcUy6Qp3U+lttuRv2HZmZmZmbWXCMWriNifUTcnZ6/ADwIjAdmAovSaouA49PzmcAVqYPlcmB3SfsARwPLImJjRGwClgEz0rJdI2J56pF5RW5fZmZmZmYdo6ZxriVNBN4JrAD6ImJ9WvQk0JeejweeyG22NqUNl762QnpdJs69sWL6mnnH1btrM2uS1eue49QKedn52KxzOB9brxl14VrSzsD3gM9ExPP5ZtEREZKiAfENjmE2WVMT+vr6KJVKFdcbGBhgzpRXKy6rtk07GhgY6Kh4q/FxmJmZWa8YVeFa0rZkBesrI+K6lPyUpH0iYn1q2rEhpa8D9s1tPiGlrQOmD0ovpfQJFdYfIiIWAAsApk2bFtOnT6+0GqVSifl3vFhx2ZqTK2/TjkqlEtWOsZP4OMzM6idpX7Kmk31AAAsi4sLUp+lqYCKwBjghIjalwQEuBI4FXgJOLTfzTIMKfCHt+ssRsSilHwxcDuwALAU+nZpsmtkojWa0EAGXAQ9GxFdzi5YA5RE/ZgHX59JPSaOGHAY8l5qP3AwcJWmP1JHxKODmtOx5SYel9zolty8zMzPLeIABsw4wmtFC3gV8BDhc0qr0OBaYBxwp6WHgfek1ZL90HwX6gW8DnwSIiI3AucCd6fGllEZa59K0zSPATQUcm5mZWdfwAANmnWHEZiERcQdQbdzpIyqsH8AZVfa1EFhYIf0u4MCRYjEzM7POGmDArNfUNFqImZmZtVarBxgY7eACZX07wJwpm4ekN7uDeLt0Sncc3R+HC9dmPUDSQuD9wIaIODCluROUWYdphwEGRju4QNnFV17P/NVDixvNHmCgXTqlO47uj2M0ba7NrPNdztCOSe4EZdZBPMCAWWdw4dqsB0TE7cDGQcnuBGXWWTzAgFkHcLMQs97lTlBmHcQDDJh1Bheuzaxps6xCbZ2h2qUj1Fi0Syedserk+Ds5djPrfC5cm/Wups+yCrV1hmqXjlBj0S6ddMaqk+Pv5NjNrPO5zbVZ73InKDMzs4K55tqsB0i6iqzWeW9Ja8lG/ZgHXCPpdOBx4IS0+lKyYfj6yYbi+yhknaAklTtBwdBOUJeTDcV3E+4EZWZmPcqFa7MeEBEnVVnkTlBmZmYFcrMQMzMzM7OCuHBtZmZmZlYQF67NzMzMzAoyYuFa0kJJGyTdl0u7Ojc71BpJq1L6REm/yi37Vm6bgyWtltQv6aI0qgCS9pS0TNLD6e8eQ4IwMzMzM+sAo6m5vhyYkU+IiD+NiKkRMRX4HnBdbvEj5WUR8Ylc+iXAx4DJ6VHe51zg1oiYDNyaXpuZmZmZdZwRC9cRcTuwsdKyVPt8AnDVcPtIE1TsGhHL00gEVwDHp8UzgUXp+aJcupmZmZlZR6l3KL4/Ap6KiIdzaZMk3QM8D3whIv4DGA+sza2zNqUB9KVJKACeBPqqvdlop00eGBhgzpRXKy7rpClxu2UKXx+HmZmZ9Yp6C9cnsXWt9XrgdyPiGUkHA/8m6YDR7iwiQlIMs3xU0yaXSiXm3/FixWWdMG1yWbdM4evjMDOrn6SFwPuBDRFxYEq7GtgvrbI78GxETJU0EXgQeCgtW15uqpmuz5eTTfq0FPh0uv7uCVwNTATWACdExKaGH5hZlxnzaCGStgH+hCwjAhARL0fEM+n5SuAR4G3AOmBCbvMJKQ3gqdRspNx8ZMNYYzIzM+til+M+UGZtr56h+N4H/DwiXm/uIemNksal528hy7SPpmYfz0s6LLXTPgW4Pm22BJiVns/KpZuZmVniPlBmnWHEZiGSrgKmA3tLWgucExGXAScyNBO/B/iSpN8ArwGfiIjyieCTbLkNdVN6AMwDrpF0OvA42cnBzMzMRq9pfaBG2/+prG8HmDNl85D0ZvdhaZd+M46j++MYsXAdESdVST+1Qtr3yG5LVVr/LuDACunPAEeMFIeZmZlV1bQ+UKPt/1R28ZXXM3/10OJGs/tAtUu/GcfR/XHU26HRzMzMWijXB+rgclpEvAy8nJ6vlDTqPlARsd59oMzGztOfm5mZdTb3gTJrIy5cm5mZdYDUB+onwH6S1qa+SlC9D9S9klYB1zK0D9SlQD/ZqF75PlBHSnqYrMA+r1HHYtbN3CzEzMysA7gPlFlncM21mZmZmVlBXLg2MzMzMyuIC9dmZmZmZgVx4drMzMzMrCAuXJuZmZmZFcSFazMzMzOzgrhwbWZmZmZWEBeuzXqcpL+UdL+k+yRdJekNkiZJWiGpX9LVkrZL626fXven5RNz+zk7pT8k6eiWHZCZmVkLjVi4lrRQ0gZJ9+XSvihpnaRV6XFsblnFC6ykGSmtX9LcXHrFi7iZNZ6k8cBfANMi4kBgHNlsb+cDF0TEW4FNQHkmuNOBTSn9grQekvZP2x0AzAC+WZ562czMrJeMpub6crKL5WAXRMTU9FgK1S+w6SL7DeAYYH/gpLQuVL+Im1lzbAPsIGkbYEdgPXA42ZTJAIuA49Pzmek1afkRkpTSF0fEyxHxGNm0yoc0J3wzM7P2MWLhOiJuBzaOcn/VLrCHAP0R8WhEvAIsBmami3K1i7iZNVhErAO+AvyCrFD9HLASeDYiNqfV1gLj0/PxwBNp281p/b3y6RW2MTMz6xnb1LHtmZJOAe4C5kTEJrKL6fLcOvkL7OAL76FkF+VqF3EzazBJe5D9KJ4EPAv8K5XvVBX5nrOB2QB9fX2USqWq6/btAHOmbB6SPtw27WJgYKAj4qymk+Pv5NjNrPONtXB9CXAuEOnvfOC0ooKqZrQX5YGBAeZMebXisk464XbLBcLH0dbeBzwWEb8EkHQd8C5gd0nbpB++E4B1af11wL7A2tSMZDfgmVx6WX6brUTEAmABwLRp02L69OlVg7v4yuuZv3roaWrNydW3aRelUonhjq3ddXL8nRz7cCQtBN4PbEh9JJD0ReBjwC/Tap/LNdU8m6yp5avAX0TEzSl9BnAhWR+LSyNiXkqfRHZneS+yO1gfSXebzawGYypcR8RT5eeSvg3ckF4Od4GtlP4M1S/ild53VBflUqnE/DterLisEy7KZd1ygfBxtLVfAIdJ2hH4FXAE2d2o24APkV1oZwHXp/WXpNc/Sct/FBEhaQnwXUlfBd4ETAZ+2swDMesBlwNfB64YlH5BRHwlnzCoD9SbgB9Kelta/A3gSLK7xXdKWhIRD7ClD9RiSd8iK5hf0qiDMetWYxqKT9I+uZcfBMojiSwBTkzDdU1iywX2TmByGhlkO7IMvyQigi0Xcdj6Im5mDRYRK8j6PNwNrCY7JywAzgL+SlI/WS3WZWmTy4C9UvpfAXPTfu4HrgEeAH4AnBERlW8fmdmYuA+UWWcYseZa0lXAdGBvSWuBc4DpkqaSNQtZA3wcsguspPIFdjO5C6ykM4GbyW5DLUwXY8gu4oslfRm4hy0XcTNrgog4hyxf5z1KhdE+IuLXwIer7Oc84LzCAzSzkTS1D1Qt/SagffpOtEvTPsfR/XGMWLiOiJMqJFctAFe7wKY2YEsrpFe8iJuZmdmImt4HqpZ+E9A+fSfapWmf4+j+OOoZLcTMzMxaqFV9oMysOheuzazjTJx745C0NfOOa0EkZq0laZ+IWJ9eDu4DVamTsUh9oMgKzycCf5Y6JlfryGxmNXDh2szMrAO4D5RZZ3Dh2szMrAO4D5RZZxjTUHxmZmZmZjaUC9dmZmZmZgVx4drMzMzMrCAuXJuZmZmZFcSFazMzMzOzgrhwbWZmZmZWEBeuzczMzMwK4sK1mZmZmVlBXLg2MzMzMyvIiIVrSQslbZB0Xy7tHyX9XNK9kr4vafeUPlHSryStSo9v5bY5WNJqSf2SLpKklL6npGWSHk5/92jAcZqZmZmZNdxoaq4vB2YMSlsGHBgRbwf+Czg7t+yRiJiaHp/IpV8CfAyYnB7lfc4Fbo2IycCt6bWZmZmZWccZsXAdEbcDGwel3RIRm9PL5cCE4fYhaR9g14hYHhEBXAEcnxbPBBal54ty6WZmZpb4TrJZZ9imgH2cBlydez1J0j3A88AXIuI/gPHA2tw6a1MaQF9ErE/PnwT6qr2RpNnAbIC+vj5KpVLF9QYGBpgz5dWKy6pt044GBgY6Kt5qfBxmZoW4HPg6WQVV2TLg7IjYLOl8sjvJZ6Vlj0TE1Ar7Kd9JXgEsJbuTfBNb7iTPkzQ3vT6rwvZmNoy6CteSPg9sBq5MSeuB342IZyQdDPybpANGu7+ICEkxzPIFwAKAadOmxfTp0yuuVyqVmH/HixWXrTm58jbtqFQqUe0YO4mPw8ysfhFxu6SJg9Juyb1cDnxouH3k7ySn1+U7yTeR3UmenlZdBJRw4dqsZmMeLUTSqcD7gZNTUw8i4uWIeCY9Xwk8ArwNWMfWTUcmpDSAp1JmL2f6DWONyczMrIedRlZILpsk6R5JP5b0RymtkDvJZlbdmGquJc0APgv8r4h4KZf+RmBjRLwq6S1kHRcfjYiNkp6XdBjZbahTgIvTZkuAWcC89Pf6MR+NmZlZD2rmneTRNtEs69sB5kzZPCS92c3s2qVpn+Po/jhGLFxLuorsNtHektYC55C16doeWJb6QSxPI4O8B/iSpN8ArwGfiIhyZ8hPkrUX24Hsl3X51/U84BpJpwOPAycUcmRmZmY9IHcn+Yj8nWTg5fR8paRR30mOiPXD3UkebRPNsouvvJ75q4cWN5rdTLNdmvY5ju6PY8TCdUScVCH5sirrfg/4XpVldwEHVkh/BjhipDjMzMxsa76TbNZ+ihgtxMzMzBrMd5LNOoML12Y9Lo2LeynZnaUg6xT1ENkQmxOBNcAJEbEpjYd7IXAs8BJwakTcnfYzC/hC2u2XI2IRZlYY30k26wxjHi3EzLrGhcAPIuL3gXcAD1J95tRj2DLL6myy8XKRtCdZLdqhwCHAOZ6AwszMepEL12Y9TNJuZLePLwOIiFci4lmqz5w6E7giMsuB3VPHp6OBZRGxMSI2kU1sMaNpB2JmZtYmXLg2622TgF8C/5zGw71U0k5UH+92PPBEbvvyGLnV0s3MzHqK21yb9bZtgIOAT0XECkkXsqUJCDDyzKm1qmWM3Grj41bSDuOk5rXL2K1j1cnxd3LsZtb5XLg2621rgbURsSK9vpascF1tvNt1wL657ctj5K5jy7TJ5fRSpTesZYzcauPjVtLsMXNH0i5jt45VJ8ffybGbWedzsxCzHhYRTwJPSNovJR0BPMCW8W5h6/FulwCnKHMY8FxqPnIzcJSkPVJHxqNSmpmZWU9xzbWZfQq4UtJ2wKPAR8l+eFca73Yp2TB8/WRD8X0UIE1McS5wZ1rvS7kxdc3MzHqGC9dmPS4iVgHTKiwaMt5tmlr5jCr7WQgsLDQ4MzOzDuNmIWZmZmZmBXHh2szMzMysIKMqXEtaKGmDpPtyaXtKWibp4fR3j5QuSRdJ6pd0r6SDctvMSus/nKZKLqcfLGl12uaiNMWymZmZmVlHGW3N9eUMnW2tyOmRLwE+ltvOM7uZmZmZWccZVeE6Im4HBvf8L2R65LRs14hYnjpLXZHbl5mZmeG7yGadop4210VNjzw+PR+cbmZmZltcju8im7W9QobiK3p65GpGO23ywMAAc6a8WnFZJ02J2y1T+Po4zMzqFxG3S5o4KHkmW2ZHXUQ2M+pZ5O4iA8slle8iTyfdRQaQVL6LXCLdRU7p5bvINzXuiMy6Uz2F66KmR16Xng9ef4jRTptcKpWYf8eLFZe12xTJw+mWKXx9HGZmDeO7yGZtpp7CdXl65HkMnR75TEmLyW47PZcK4DcDf5+7/XQUcHaa2e35NJXyCuAU4OI64jIzM+s57XYXuaxvB5gzZfOQ9GbfCWyXu4+Oo/vjGFXhWtJVZLXOe0taS9Zeax7FTY/8SbK2ZDuQ3YLybSgzM7ORte1d5LKLr7ye+auHFjeafSe5Xe4+Oo7uj2NUheuIOKnKokKmR46Iu4ADRxOLmZmZvc53kc3aTCEdGs3MzKyxfBfZrDO4cG1mZtYBfBfZrDPUM861mZmZmZnluHBtZmZmZlYQF67NzMzMzAriwrWZmZmZWUFcuDYzMzMzK4gL12ZmZmZmBXHh2szMzMysIC5cm5mZmZkVxIVrMzMzM7OCuHBtZmZmZlYQF67NDEnjJN0j6Yb0epKkFZL6JV0tabuUvn163Z+WT8zt4+yU/pCko1t0KGZmZi015sK1pP0krco9npf0GUlflLQul35sbpuKF19JM1Jav6S59R6UmdXs08CDudfnAxdExFuBTcDpKf10YFNKvyCth6T9gROBA4AZwDcljWtS7GZmZm1jzIXriHgoIqZGxFTgYOAl4Ptp8QXlZRGxFKpffNMF+BvAMcD+wElpXTNrAkkTgOOAS9NrAYcD16ZVFgHHp+cz02vS8iPS+jOBxRHxckQ8BvQDhzTlAMx6nCu7zNrLNgXt5wjgkYh4PLvOVvT6xRd4TFL+4tsfEY8CSFqc1n2goNjMbHhfAz4L7JJe7wU8GxGb0+u1wPj0fDzwBEBEbJb0XFp/PLA8t8/8NmbWQBHxEDAVsiZewDqyyq6PklV2fSW//qDKrjcBP5T0trT4G8CRZHn4TklLIsLXY7MaFFW4PhG4Kvf6TEmnAHcBcyJiE8NffJ8YlH5opTeRNBuYDdDX10epVKoYzMDAAHOmvFpxWbVt2tHAwEBHxVuNj6N9SXo/sCEiVkqa3qT3HFU+BujbAeZM2Vx1eV67/W86/fvSyfF3cuwFcGWXWYvVXbhOHZ0+AJydki4BzgUi/Z0PnFbv+wBExAJgAcC0adNi+vTpFdcrlUrMv+PFisvWnFx5m3ZUKpWodoydxMfR1t4FfCDdLn4DsCtwIbC7pG1S7fUEspow0t99gbWStgF2A57JpZflt9nKaPMxwMVXXs/81aM7TbVb3u7070snx9/JsRegKZVdZlZdETXXxwB3R8RTAOW/AJK+DdyQXg538R3VRdnMihURZ5N+GKea67+OiJMl/SvwIWAxMAu4Pm2yJL3+SVr+o4gISUuA70r6Ktlt5snAT5t4KGY9r1mVXbXcfYLqd6CafXehXe5oOI7uj6OIwvVJ5H4lS9onItanlx8E7kvPq118BUyWNImsUH0i8GcFxGVmY3cWsFjSl4F7gMtS+mXAd9Jt5I1k+ZWIuF/SNWS3jzcDZ0RE5bZZZtYoTansquXuE1S/A9Xsu03tckfDcXR/HHUVriXtRNbx4eO55H+QNJXsl/Ka8rLhLr6SzgRuBsYBCyPi/nriMrPaRUQJKKXnj1JhtI+I+DXw4Srbnwec17gIzWwEruwyawN1Fa4j4kWykQLyaR8ZZv2KF980XN/SemIxMzPrVa7sMmsfRY0WYmZmZi3iyi6z9uHpz83MzMzMCuLCtZmZmZlZQVy4NjMzMzMriAvXZmZmZmYFceHazMzMzKwgLlybmZmZmRXEhWszMzMzs4K4cG1mZmZmVhAXrs3MzMzMCuLCtZmZmZlZQVy4NjMzMzMrSN2Fa0lrJK2WtErSXSltT0nLJD2c/u6R0iXpIkn9ku6VdFBuP7PS+g9LmlVvXGZmZmZmzVZUzfV7I2JqRExLr+cCt0bEZODW9BrgGGByeswGLoGsMA6cAxwKHAKcUy6Qm5mZ2fBc0WXWPhrVLGQmsCg9XwQcn0u/IjLLgd0l7QMcDSyLiI0RsQlYBsxoUGxmZmbdyBVdZm2giMJ1ALdIWilpdkrri4j16fmTQF96Ph54Irft2pRWLd3MzMzGxhVdZi2wTQH7eHdErJP028AyST/PL4yIkBQFvA+p8D4boK+vj1KpVHG9gYEB5kx5teKyatu0o4GBgY6Ktxofh5lZw5UrugL4p4hYgCu6zFqi7sJ1RKxLfzdI+j7ZraSnJO0TEevTr+ENafV1wL65zSektHXA9EHppQrvtQBYADBt2rSYPn364FWArAA9/44XKy5bc3LlbdpRqVSi2jF2Eh+HmVnDtV1FV1nfDjBnyuYh6c2urGiXChLH0f1x1FW4lrQT8FsR8UJ6fhTwJWAJMAuYl/5enzZZApwpaTFZm67nUgH8ZuDvc227jgLOric2MzOzXtGOFV1lF195PfNXDy1uNLuyq10qSBxH98dRb5vrPuAOST8DfgrcGBE/ICtUHynpYeB96TXAUuBRoB/4NvBJgIjYCJwL3JkeX0ppZmZmNgxJO0napfycrILqPrZUdMHQiq5T0qghh5EquoCbgaMk7ZEqu45KaWZWg7pqriPiUeAdFdKfAY6okB7AGVX2tRBYWE88ZmZmPagP+L4kyK7r342IH0i6E7hG0unA48AJaf2lwLFkFV0vAR+FrKJLUrmiC1zRZTYmRXRoNDMzsxZxRZdZe/H052ZmZmZmBXHh2qyHSdpX0m2SHpB0v6RPp3TP7GZmZjYGLlyb9bbNwJyI2B84DDhD0v54ZjczM7MxceHarIdFxPqIuDs9fwF4kGzSCM/sZmZmNgbu0GhmAEiaCLwTWEEDZ3arZQKKapNPVNIOkxDktcvECGPVyfF3cuxm1vlcuDYzJO0MfA/4TEQ8n4b0Aoqd2S3tb9QTUFSbfKKSdpt9tV0mRhirTo6/k2M3s87nZiFmPU7StmQF6ysj4rqU/FRq7kENM7tVSjczM+spLlyb9TBlVdSXAQ9GxFdzizyzm5mZ2Ri4WYhZb3sX8BFgtaRVKe1zwDw8s5uZmVnNXLg262ERcQegKos9s5uZmVmNXLg2s64wce6NFdPXzDuuyZGYmVkvG3Ob62FmdvuipHWSVqXHsbltzk4zuz0k6ehc+oyU1i9pbqX3MzMzMzNrd/V0aKw2sxvABRExNT2WAqRlJwIHkE0u8U1J4ySNA75BNvPb/sBJuf2YmZnZMFzZZdZextwsJI0QsD49f0FSeWa3amYCiyPiZeAxSf1k0yQD9EfEowCSFqd1HxhrbGZmZj2kXNl1t6RdgJWSlqVlF0TEV/IrD6rsehPwQ0lvS4u/ARxJNhHUnZKWRISvx2Y1KGQovkEzuwGcKeleSQvTsFxQwMxuZmZmtrWIWB8Rd6fnLwCjruyKiMfIRv85JD36I+LRiHgFKFd2mVkN6u7QWGFmt0uAc4FIf+cDp9X7Pum9RjVt8sDAAHOmvFpxWSdNidstU/j6OMzMmmNQZde7yCq7TgHuIqvd3kRW8F6e2yxfqTW4suvQRsds1m3qKlxXmtktIp7KLf82cEN6OdwMbqOa2W200yaXSiXm3/FixWXtNkXycLplCl8fh5lZ4zWrsmu0FV1lfTvAnCmbh6Q3u7KiXSpIHEf3xzHmwnW1md0k7ZPaYwN8ELgvPV8CfFfSV8naeE0Gfko2xu5kSZPICtUnAn821rjMzMx6TTMru0Zb0VV28ZXXM3/10OJGsyu72qWCxHF0fxz11FxXm9ntJElTyX4prwE+DhAR90u6hqyj4mbgjIh4FUDSmWRTJY8DFkbE/XXEZWZm1jNc2WXWXuoZLaTazG5Lh9nmPOC8CulLh9vOzMzMqnJll1kb8QyNZmZmHcyVXWbtpZCh+MzMzMzMzIVrMzMzM7PCuHBtZmZmZlYQF67NzMzMzAriwrWZmZmZWUFcuDYzMzMzK4gL12ZmZmZmBXHh2szMzMysIJ5Exsy62sS5N1ZMXzPvuCZHYmZmvcA112ZmZmZmBXHh2szMzMysIC5cm5mZmZkVpG3aXEuaAVwIjAMujYh5jXgft780a5xm5WMzaxznY7P6tEXhWtI44BvAkcBa4E5JSyLigdZGZmaj1Wn5uNIPbf/Itl7XafnYrB21ReEaOAToj4hHASQtBmYCzsxmnaPj87HvbJl1fj42a7V2KVyPB57IvV4LHNrMAFyLZVa3lufjRqlW6K7E5w3rcF2bj82apV0K16MiaTYwO70ckPRQlVX3Bp6u+/3Or3cPdSvkONqAj2Ps3tzk92u4GvIxdOB3J3fe6LjYB+nk+Nst9q7KxzXmYajy/2jBNbZdvheOY2udEEdNebhdCtfrgH1zryektK1ExAJgwUg7k3RXREwrLrzW8HG0l245jgYqNB9DZ3/mnRw7dHb8nRx7GxgxH9eSh6F9/h+Ow3E0K452GYrvTmCypEmStgNOBJa0OCYzq43zsVnncz42q1Nb1FxHxGZJZwI3kw39szAi7m9xWGZWA+djs87nfGxWv7YoXANExFJgaUG7G/Xtqjbn42gv3XIcDVNwPobO/sw7OXbo7Pg7OfaW6+J87Di25ji2Vlgcioii9mVmZmZm1tPapc21mZmZmVnH66rCtaQZkh6S1C9pbqvjqYWkhZI2SLovl7anpGWSHk5/92hljCORtK+k2yQ9IOl+SZ9O6Z12HG+Q9FNJP0vH8XcpfZKkFen7dXXq7GMN0i75uZa8qcxFKeZ7JR2U22ZWWv9hSbNy6QdLWp22uUiSCoy9pjzZTvHXmg8lbZ9e96flE3P7OjulPyTp6Fx6W3zHulUzP99hvutflLRO0qr0ODa3TcXvRQGxrEl5YpWku1JazXmuzhj2yx3zKknPS/pMMz4PNficWWcc/yjp5+m9vi9p95Q+UdKvcp/Lt3Lb1H6Oi4iueJB1vHgEeAuwHfAzYP9Wx1VD/O8BDgLuy6X9AzA3PZ8LnN/qOEc4hn2Ag9LzXYD/AvbvwOMQsHN6vi2wAjgMuAY4MaV/C/i/rY61Wx/tlJ9ryZvAscBN6Tt0GLAipe8JPJr+7pGe75GW/TStq7TtMQXGXlOebKf4a82HwCeBb6XnJwJXp+f7p+/P9sCk9L0a107fsW58NPvzHea7/kXgryusX/F7UVAsa4C9B6XVlOca8L94kmys5oZ/HjT4nFlnHEcB26Tn5+fimJhfb9B+aj7HdVPN9etTtkbEK0B5ytaOEBG3AxsHJc8EFqXni4DjmxlTrSJifUTcnZ6/ADxINttXpx1HRMRAerltegRwOHBtSm/74+hwbZOfa8ybM4Er0ndoObC7pH2Ao4FlEbExIjYBy4AZadmuEbE8srP4FRT4vRpDnmyb+MeQD/PHdC1wRKphmgksjoiXI+IxoJ/s+9U237Eu1dTPd5jvejXVvheNUmueK9IRwCMR8fgI8RXyeTTynFlvHBFxS0RsTi+Xk43jXtVYz3HdVLiuNGXrcBmrE/RFxPr0/Emgr5XB1CLdkn0nWW1Txx2HpHGSVgEbyDL1I8CzuUzZDd+vdtbu+bnad7pa3MOlr62QXrhR5sm2ir/GfPh6jGn5c8BeYzgmK0bLPt9B33WAM1MzgIXa0iyxkfEFcIuklcpms4Ta81yRTgSuyr1u9ucBxZ1zinQaWU102SRJ90j6saQ/ysVX8zmumwrXXS39YuqIoV0k7Qx8D/hMRDyfX9YpxxERr0bEVLJftYcAv9/aiKxddcJ3ulPzpPOh1arCd/0S4PeAqcB6YH4Twnh3RBwEHAOcIek9+YXNzHPK+iR8APjXlNSKz2Mr7XDOkfR5YDNwZUpaD/xuRLwT+Cvgu5J2Hev+u6lwPaqplzvMU+XbQ+nvhhbHMyJJ25Kd2K6MiOtScscdR1lEPAvcBvwh2e2q8tjw3fD9amftnp+rfaerxT1c+oQK6YWpMU+2Xfww6nz4eoxp+W7AMyPE3s7fsU7X9M+30nc9Ip5KP9JeA77NlqYODYsvItalvxuA76f3rDXPFeUY4O6IeCrF1PTPIynqnFM3SacC7wdOTgV9UnOYZ9LzlWR3yd7GGM9x3VS47sYpW5cA5R6ys4DrWxjLiFL7xsuAByPiq7lFnXYcb8z1IN4BOJKs/d5twIfSam1/HB2u3fNzte/0EuCU1AP+MOC5dCv0ZuAoSXuk27BHATenZc9LOizln1Mo8Hs1hjzZNvGPIR/mj+lDwI/ShXMJcKKy0UQmAZPJOii1+3es0zX18632XR/UfvmDQHnkiGrfi3rj2EnSLuXnZHnlPmrPc0U5iVyTkGZ/HjmFnHPqDULSDOCzwAci4qVc+hsljUvP30J2/I+O+RwXBfdKbeWDrNfpf5H94vh8q+OpMfaryG5L/IasTc/pZO0FbwUeBn4I7NnqOEc4hneT3eq5F1iVHsd24HG8HbgnHcd9wN+m9LeQnWz6yW6xbd/qWLv50S75uZa8Sdab/Bsp5tXAtNx+TkvfnX7go7n0ael79gjwddLkXgXFXlOebKf4a82HwBvS6/60/C25fX0+xfcQuZ7+7fId69ZHMz/fYb7r30nf5XvJCnL7jPS9qDOOt5CNuvEz4P7ycY8lzxUQy05kd292y6U1/POgwefMOuPoJ2vLXf6OlEcY+t/p/7UKuBv449x+aj7HeYZGMzMzM7OCdFOzEDMzMzOzlnLh2szMzMysIC5cm5mZmZkVxIVrMzMzM7OCuHBtZmZmZlYQF67NzMzMzAriwrWZmZmZWUFcuDYzMzMzK8j/D+O5pOZ0WDNpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capture variable names in a list\n",
    "\n",
    "variables = list(X.columns)\n",
    "\n",
    "variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "\n",
    "def make_qqplot(df):\n",
    "\n",
    "    plt.figure(figsize=(10, 6), constrained_layout=True)\n",
    "\n",
    "    for i in range(6):\n",
    "\n",
    "        # location in figure\n",
    "        ax = plt.subplot(2, 3, i + 1)\n",
    "\n",
    "        # variable to plot\n",
    "        var = variables[i]\n",
    "\n",
    "        # q-q plot\n",
    "        stats.probplot((df[var]), dist=\"norm\", plot=plt)\n",
    "\n",
    "        # add variable name as title\n",
    "        ax.set_title(var)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_qqplot(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yeo-Johnson transformation with Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize a transformer with yeo-johnson\n",
    "\n",
    "transformer = PowerTransformer(\n",
    "    method=\"yeo-johnson\", standardize=False).set_output(transform=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PowerTransformer(standardize=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PowerTransformer(standardize=False)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PowerTransformer(standardize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit transformer: transformer will learn the lambdas\n",
    "\n",
    "transformer.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.19850989,  0.80814809, -0.5536698 , -4.39408222,  0.23352364,\n",
       "       -0.90134563])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lambdas are stored in a transformer attribute\n",
    "\n",
    "transformer.lambdas_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.803599</td>\n",
       "      <td>24.133444</td>\n",
       "      <td>1.234372</td>\n",
       "      <td>0.217303</td>\n",
       "      <td>12.223224</td>\n",
       "      <td>0.755821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.801958</td>\n",
       "      <td>13.807367</td>\n",
       "      <td>1.202460</td>\n",
       "      <td>0.216060</td>\n",
       "      <td>22.087944</td>\n",
       "      <td>0.710445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.724579</td>\n",
       "      <td>29.380778</td>\n",
       "      <td>1.280312</td>\n",
       "      <td>0.218341</td>\n",
       "      <td>13.970688</td>\n",
       "      <td>0.776570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.578385</td>\n",
       "      <td>29.380778</td>\n",
       "      <td>1.182107</td>\n",
       "      <td>0.218334</td>\n",
       "      <td>14.478725</td>\n",
       "      <td>0.755137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.354895</td>\n",
       "      <td>29.380778</td>\n",
       "      <td>1.204470</td>\n",
       "      <td>0.218489</td>\n",
       "      <td>14.533326</td>\n",
       "      <td>0.718551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MedInc   HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  1.803599  24.133444  1.234372   0.217303   12.223224  0.755821\n",
       "1  1.801958  13.807367  1.202460   0.216060   22.087944  0.710445\n",
       "2  1.724579  29.380778  1.280312   0.218341   13.970688  0.776570\n",
       "3  1.578385  29.380778  1.182107   0.218334   14.478725  0.755137\n",
       "4  1.354895  29.380778  1.204470   0.218489   14.533326  0.718551"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform data: returns NumPy array\n",
    "\n",
    "X_tf = transformer.transform(X)\n",
    "\n",
    "X_tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X_tf.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "make_qqplot(X_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yeo-Johnson transformation with Feature-engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>YeoJohnsonTransformer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;YeoJohnsonTransformer<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>YeoJohnsonTransformer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "YeoJohnsonTransformer()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up the transforme: automatically identifies numerical variables\n",
    "\n",
    "yjt = YeoJohnsonTransformer()\n",
    "\n",
    "# fit transformer to the dataframe\n",
    "\n",
    "yjt.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MedInc': -0.1985099326842257,\n",
       " 'HouseAge': 0.8081480486309912,\n",
       " 'AveRooms': -0.5536698123497441,\n",
       " 'AveBedrms': -4.394082228792883,\n",
       " 'Population': 0.2335235944994063,\n",
       " 'AveOccup': -0.9013456049848348}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the exponents for each variable\n",
    "\n",
    "yjt.lambda_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform variables: returns a new dataframe\n",
    "\n",
    "X_tf = yjt.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X_tf.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "make_qqplot(X_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yeo-Johnson transformation with SciPy\n",
    "\n",
    "One variable at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the dataframe where we will store the modified\n",
    "# variables\n",
    "\n",
    "X_tf = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal λ:  -0.1985099326842257\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apply the Box-Cox transformation to variable MedInc\n",
    "X_tf[\"MedInc\"], param = stats.yeojohnson(X[\"MedInc\"])\n",
    "\n",
    "# print the optimal lambda found for MedInc\n",
    "print(\"Optimal λ: \", param)\n",
    "\n",
    "# visualize the transformed variable\n",
    "X_tf[\"MedInc\"].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "921.556px",
    "left": "0px",
    "right": "1693px",
    "top": "110.444px",
    "width": "204.333px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
